Hyperframes Media

HyperFrames Media Preprocessing

Three CLI commands that produce assets for compositions: tts (speech), transcribe (timestamps), and remove-background (transparent video). Each downloads a model on first run and caches it under ~/.cache/hyperframes/.

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Text-to-Speech (tts)

Generate speech audio locally with Kokoro-82M. No API key required.

npx hyperframes tts "Text here" --voice af_nova --output narration.wav
npx hyperframes tts script.txt --voice bf_emma --output narration.wav
npx hyperframes tts --list                       # list all 54 voices

Voice Selection

Content Type	Recommended Voices	Why
Product demo	af_heart / af_nova	Warm, professional
Tutorial / how-to	am_adam / bf_emma	Neutral, easy to follow
Marketing / promo	af_sky / am_michael	Energetic or authoritative
Documentation	bf_emma / bm_george	Clear British English, formal
Casual / social	af_heart / af_sky	Approachable, natural

Multilingual

Voice IDs encode language in the first letter:

• a = American English, b = British English, e = Spanish
• f = French, h = Hindi, i = Italian, j = Japanese
• p = Brazilian Portuguese, z = Mandarin

The CLI auto-detects the phonemizer locale from the prefix — no --lang needed when the voice matches the text.

npx hyperframes tts "La reunión empieza a las nueve" --voice ef_dora --output es.wav
npx hyperframes tts "今日はいい天気ですね" --voice jf_alpha --output ja.wav

Use --lang only to override auto-detection (stylized accents). Valid codes: en-us, en-gb, es, fr-fr, hi, it, pt-br, ja, zh.

Speed

Speed	Use Case
0.7-0.8	Tutorial, complex content, accessibility
1.0	Natural pace (default)
1.1-1.2	Intros, transitions, upbeat content
1.5+	Rarely appropriate; test carefully

Long Scripts

Write to a .txt file and pass the path. Inputs over ~5 minutes may benefit from splitting into segments.

Requirements

Python 3.8+ with kokoro-onnx and soundfile (pip install kokoro-onnx soundfile). Model downloads on first use (~311 MB + ~27 MB voices, cached in ~/.cache/hyperframes/tts/).

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Transcription (transcribe)

Produce a normalized transcript.json with word-level timestamps.

npx hyperframes transcribe audio.mp3
npx hyperframes transcribe video.mp4 --model small --language es
npx hyperframes transcribe subtitles.srt          # import existing
npx hyperframes transcribe subtitles.vtt
npx hyperframes transcribe openai-response.json

Critical Language Rule

Never use .en models unless the user explicitly states the audio is English. .en models (small.en, medium.en) translate non-English audio into English instead of transcribing it. This silently destroys the original language.

1. Language known and non-English → --model small --language <code> (no .en suffix)
2. Language known and English → --model small.en
3. Language unknown → --model small (no .en, no --language) — whisper auto-detects

Default model is small, not small.en.

Model Sizes

Model	Size	Speed	When to use
tiny	75 MB	Fastest	Quick previews, testing pipeline
base	142 MB	Fast	Short clips, clear audio
small	466 MB	Moderate	Default — most content
medium	1.5 GB	Slow	Important content, noisy audio, music
large-v3	3.1 GB	Slowest	Production quality

Music with vocals: start at medium minimum.

Output Shape

[
  { "id": "w0", "text": "Hello", "start": 0.0, "end": 0.5 },
  { "id": "w1", "text": "world.", "start": 0.6, "end": 1.2 }
]

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Background Removal (remove-background)

Remove the background from a video or image so the subject sits as a transparent overlay.

npx hyperframes remove-background subject.mp4 -o transparent.webm  # VP9 alpha WebM
npx hyperframes remove-background subject.mp4 -o transparent.mov   # ProRes 4444
npx hyperframes remove-background portrait.jpg -o cutout.png       # single-image cutout
npx hyperframes remove-background subject.mp4 -o subject.webm \
  --background-output plate.webm                                   # both layers
npx hyperframes remove-background --info                           # detected providers

Uses u2net_human_seg (MIT). First run downloads ~168 MB of weights.

Layer Separation (--background-output)

Pass --background-output (or -b) to emit a second transparent video with the inverse alpha:

File	Alpha is...	Use it for
-o subject.webm	The mask — subject opaque, bg transparent	Foreground layer
--background-output plate.webm	Inverse — bg opaque, subject transparent	Bottom layer; put text/graphics between

Both share the same quality preset and run from a single inference pass.

Output Format

Format	When
.webm (VP9 + alpha)	Default. Compositions play directly via <video>.
.mov (ProRes 4444)	Editing in DaVinci/Premiere/FCP. Large files.
.png	Single-image cutout.

Quality Presets

Preset	CRF	When
fast	30	Iterating, smaller file
balanced	18	Default. Visually identical for most uses
best	12	Master / final delivery

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TTS -> Transcribe -> Captions Pipeline

Generate voiceover, get word-level timestamps, and create captions:

npx hyperframes tts script.txt --voice af_heart --output narration.wav
npx hyperframes transcribe narration.wav   # -> transcript.json

Whisper extracts precise word boundaries from the generated audio, so caption timing matches delivery without hand-tuning.

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Related Skills

Skill	Purpose
hyperframes	Composition authoring (HTML, GSAP, captions, variables)
hyperframes-cli	CLI dev loop (init, lint, preview, render, doctor)